Skip to content

Advertisement

  • Research
  • Open Access

A structural equation modeling analysis of relational governance and economic performance in agri-food supply chains: evidence from the dairy sheep industry in Sardinia (Italy)

Agricultural and Food Economics20186:4

https://doi.org/10.1186/s40100-018-0099-z

  • Received: 17 March 2017
  • Accepted: 1 February 2018
  • Published:

Abstract

This study investigates the factors affecting the inter-organizational relationships and governance of firms in agri-food supply chains and assesses the influence that the current conditions of vertical coordination have on the economic performance of these firms.

Research hypotheses describing the causal effects between the environment, product characteristics, inter-organizational relationships, relational governance, and firm economic performance are formulated and tested using a structural equation modeling approach. Data were gathered from a questionnaire administered via a direct survey to both farmers and processors in a traditional high-quality dairy sheep supply chain in the Italian region of Sardinia: the Pecorino Romano Protected Designation of Origin.

Results point out the role of informal contractual arrangements in this local production system characterized by social cohesion, entailing higher product quality and better economic performance. Further, the study highlights the role of trust as a key variable for attaining collaborative paths along the agri-food supply chain, particularly between farmers and processors.

Keywords

  • Relational governance
  • Economic performance
  • Agri-food supply chain
  • Vertical coordination
  • Structural equation model
  • Direct survey
  • Pecorino Romano
  • Protected designation of origin (PDO)
  • Marketing

Background

The competitive environment in which agri-food firms operate has become increasingly complex. The numerous factors contributing to the increasing complexity include technological progress, globalization, and the structure of the industry itself (Albisu et al. 2010), which is characterized by the perishable nature of the products (Aramyan and Kuiper 2009) as well as the large number of small- and medium-sized enterprises compared to the small number of suppliers and buyers that often exert market power. Consequently, many companies are now induced to abandon the traditional spot market exchange modality for new and more complex systems of negotiation and coordination.

Hence, a growing number of studies have been focusing on the complex issue of supply chain organization, particularly on the most appropriate inter-organizational relationships and modes of governance that firms should adopt in order to improve their economic performance. The numerous practices that companies adopt include the use of market supply contracts or sales agreements in short- and long-term collaboration or, more commonly these days, a combination of both. In fact, along the continuum between market and hierarchy, there are many intermediate forms of governance, known as hybrid forms, which range from strategic alliances to supply contracts and from partnerships to cooperative agreements between companies (Fischer et al. 2009; Ménard 2004). The growing industrialization of the food sector and the consequent increase in the adoption of vertical forms of coordination and consolidation suggest a need to develop more specific analyses of the relational dynamics in the agri-food chain. The formation of new types of alliances and networks also creates the need for a deeper understanding of emerging intermediate forms of governance between market and hierarchy and the relationships that are established between firms and organizations (Carbone 2017; Chaddad and Rodriquez-Alcala 2010).

In this respect, the dairy sheep sector in the European Mediterranean basin represents an interesting case study, as pasture-based sheep farming systems are mostly located in marginal areas and have an important economic and social role in rural development (Bernués et al. 2011; Riveiro et al. 2013). Nevertheless, the sector has suffered a strong decline in most regions in terms of both holdings and animals (Ripoll-Bosch et al. 2012).

Sheepherding and cheese production have a long tradition in Italy as well, especially in the central and southern regions. This is the case of Sardinia, an island in which the dairy sheep sector is particularly important (Furesi et al. 2013).

The socioeconomic importance of the dairy-sheep supply chain is noteworthy especially at the farming stage. In fact, according to the latest official statistics (ISTAT 2012), Sardinia farms breed approximately three million sheep (44% of the total Italian sheep livestock) and provide 56% of the total sheep milk produced in Italy. However, among the 12,000 sheep breeding farms, only 5000 are specialized (ISTAT 2012), and despite the increasing average herd size (about 240 heads per farm), the majority of businesses are still family run and predominantly (or exclusively) make direct use of the labor available within their family (Pulina et al. 2011). Unlike in the past, only a small portion of milk is now processed into cheese on farms, with the rest being supplied to processing firms (Furesi et al. 2013; Idda et al. 2010).

On the other hand, at the cheese manufacturing stage, a noteworthy market shares concentration is observed, as compared to the farming stage. In fact, only six processors account for about 80% of the total amount of cheese produced on the island (estimated at approximately 70,000 tons in 2010). A further significant feature of the processing stage concerns the presence of both cooperatives and private companies. The 25 cooperatives operating in Sardinia have, for a long time, played a crucial role in the region’s socioeconomic development. Recently, however, they have begun to face some difficulties resulting from their statutory goal to ensure an adequate return to their members. This entails a low level of profitability, limited investments, and limited innovation capabilities. As for private processing firms, their combined market share is estimated at approximately 60% of the total cheese production. Private firms have shown substantial vitality and a reduced rate of cessation over the past 10 years. The capitalist nature of such companies and their purpose of maximizing profits and returns on invested capital favor investment plans targeting a continuous modernization of production processes. The hierarchical and top-down components of these organizations also allow for a more streamlined management of strategic operations and production.

Despite the variety of dairy products available, Protected Designation of Origin (PDO) cheeses hold the largest market share. These include Pecorino Romano, Pecorino Sardo, and Fiore Sardo. Among them, Pecorino Romano has become the leading product in terms of both the value of production (over 60% of the total cheese production in Sardinia) and exports. Pecorino Romano is, in fact, the third most exported Italian cheese, following Parmigiano Reggiano and Grana Padano. The USA is the main export destination, where it is used mostly as an ingredient by the food industry. The PDO consortia also play a crucial role downstream in the supply chain, particularly in the processing stage, as 66 processors are members of the consortia. Although most milk transactions today are formalized between farmers and processors by contracts that are based exclusively on the parameters of price and quantity (i.e., issues of quality are not included), important differences exist between processing cooperatives and private industrial processors. Private companies usually impose closed contracts in which prices are determined; alternatively, the price of milk may be set at the beginning of the marketing year. Private industrial processors are, in fact, in a privileged position because they have access to a range of information about markets, and they possess greater bargaining power because of their large market shares. However, these processors also assume most of the risk by bearing potential losses due to negative weather conditions or unexpected events, as there is no contractual provision of risk sharing. In contrast, cooperative processors establish the price only ex-post or during the profits distribution process, thus shifting the risk completely to their members.

Examples of positive relationships between operators include those wherein cooperatives provide their members with technical assistance or inputs such as feed and labor and those wherein individual processors take part in farm management decisions or offer services such as milk-quality analysis to their suppliers.

The performance of the whole supply chain has changed significantly over the last 50 years. For a long time, sheep farming was a subsistence activity in Sardinia. However, this condition changed during the 1970s, when the price of sheep products increased, driven mainly by the development of high-quality cheeses, especially the Pecorino Romano Protected Designation of Origin (PDO).

For a long period thereafter, stable consumer preferences, long-standing trade agreements, and the consolidated Pecorino Romano brand ensured favorable market conditions for both farmers and processors, both in the domestic and foreign markets.

Starting from 2008, both cheese and milk price started to suffer the unfavorable conditions on foreign markets, mostly due to the financial crisis and the competition of other cheaper cheeses in the USA. Later in 2015, the recovery in consumption and the evolution of the euro/dollar exchange rate stimulated the US demand for imported cheeses in general and, in particular, for Pecorino Romano, whose export value increased by 20% over the previous year. Since this trend provided a strong incentive for farmers to expand milk production, processors forecasted a strong oversupply and renegotiated milk price during the campaign.

While oversupply has been confirmed, there is still no objective and shared assessment of its size. The lack of information and trust is often pointed out as the main reason for the failure of supply chain coordination that still leads to very low milk prices (ranging from 0.50€/lt to 0.65€/lt), thus jeopardizing the economic sustainability of many small family farms.

Despite these problems, this sector has been the topic of few studies, with the large majority of them focusing almost exclusively on the issues of production, technical efficiency, and profitability (Rancourt and Carrère 2011; Frendi et al. 2011; Furesi et al. 2013; Mantecón et al. 2009; Milàn et al. 2011; Toussaint et al. 2009), whereas to our knowledge, little attention has been paid to relational aspects within the supply chain in the economic literature.

In light of these considerations, this paper evaluates the determinants and effects of relational governance on firm performance in agri-food chains. More precisely, it investigates the factors affecting inter-organizational relationships and governance and assesses the influence that the current conditions of vertical coordination and collaboration have on the economic performance of firms in the traditional dairy sheep supply chain in Sardinia (Italy).

The methodological approach of the study is described in the “Methods” section, including the formulation of a set of research hypotheses (“Research hypotheses” section) and the collection and treatment of empirical evidence by means of a direct survey (“Data and measures” section). The “Results and discussion” section presents and discusses the results of the model estimations. Finally, we provide some concluding remarks (“Conclusions” section) and some considerations concerning the limitations of the study and future research prospects (“Limitations of the study and prospects for future research” section).

Methods

The methodology adopted follows a structural equation modeling (SEM) approach and includes the following steps.

First, based on both a critical review of the relevant literature and interviews conducted with a panel of experts in the Sardinian dairy sheep supply chain (i.e., regional authority officers, presidents of PDO consortia, and members of trade associations), we formulated three sets of research hypotheses, targeting the relationships between the three groups of exogenous factors (namely environmental conditions, product characteristics, and producer-processor relationships) and the two main variables of interest, i.e., relational governance and firm economic performance.

As a second step of the analysis, we set up a questionnaire assessing all the relevant factors included in the research hypotheses and carried out a direct survey of 96 firms in the dairy sheep supply chain in Sardinia to empirically test their relationships.

As a third step, an exploratory factor analysis (EFA) was conducted to determine what items should be included in the models and to assess the number and the validity of the underlying multidimensional constructs.

Then, we framed and estimated a SEM per each set of hypotheses formulated following a linear structural relation approach (Jöreskog and van Thillo 1972). Specifying the model followed an iterative process based on theoretical and empirical analyses until the structural model fit was positively tested.

Research hypotheses

The basic research hypotheses formulated in this study concern the causal relationship between a number of exogenous factors and the types of relational governance adopted by farmers and their economic performance.

Relational governance (i.e., the governance of dyadic relationships) in the supply chain can be defined as the set of practices and behaviors that both sides adopt in order to achieve common goals and to ensure stable relationships (Carr and Pearson 1999). More precisely, relational governance refers to the intermediary mode between market and hierarchy for coordinating various economic activities within the supply chain (Claro et al. 2003), and it is evidenced by mechanisms that can be either (a) relational or (b) transactional in nature (Liu et al. 2009). On the one hand, relational mechanisms (a) focus on the roles of social interactions in economic activities. They also govern exchanges through social standards of expected behaviors that prevent the need for, and are more effective than, purely authoritarian relationships. In fact, social mechanisms have been recognized as effective in controlling opportunism and constructing cooperative behavior in buyer-supplier relationships, especially in local production systems (Granovetter 1992) where social cohesion is strong and where less formal contractual arrangements can be effectively adopted, granting flexibility and reducing transaction costs (Farrell 2005).

On the other hand, transactional mechanisms (b) involve bilateral contractual clauses and specific investments that complement each other, since contracts specify conditions and governance measures that are not covered specifically in investments, whereas specific investments provide extra-economic incentives for an on-going relationship that may not be included in contracts.

The definition and measurement of economic performance has been extensively studied in the economic literature.

Based on the findings of previous research, there is a shared consensus about the expected positive influence of relational governance on performance, thanks to the reduction of transaction costs entailed by interpersonal trust, joint planning, and problem solving (Claro et al. 2003; Zaheer et al. 1998).

The most commonly used indicators of business performance in the context of supply chain management include market data (e.g., selling prices, market shares, etc.), efficiency or financial data (e.g., cots, profit, return on assets, return on sales, etc.), product quality (including sensory properties, shelf life, safety, and convenience), and responsiveness (customer service levels, lead time, etc.) (Aramyan et al. 2007; Kannan and Tan 2005; Zaheer et al. 1998).

According to various contributions in the economic literature, firm size is also an important factor affecting both the type of governance of supply chain dyadic relationships and firm economic performance. The firm size-economic performance relation shows ambiguous results where the size of the firm can positively influence the chain performance due to scale economies (allocative efficiency) while smaller firms can provide a more efficient input use (technical efficiency) (Gereffi et al. 2008; Aramyan 2007).

Three further relevant groups of factors affecting the type of governance adopted and firm economic performance are pointed out in the literature reviewed. These include the following: (i) environmental conditions, (ii) product features, and (iii) relationship among actors.

The environment in which the company operates has significant effects on how it competes and cooperates with other firms within the supply chain. In this regard, Fontana and Caroli (Fontana and Caroli 2003) offer an interesting descriptive model of the environment, articulating the study at three different levels of analysis: extended environment, competitive environment, and business-specific environment. The extended environment represents the complex of conditions and subjects that characterize the wider reality of the firm (i.e., the institutional environment). The competitive environment consists of actors and conditions that directly affect the strategic and operational behavior of the firm. The specific environment represents the factors directly relevant to a particular business area. Firms’ relationships are shaped according to the conditions that characterize the environment at different levels. These conditions can be grouped into four general categories: economic condition, technological condition, institutional political condition, and socio-cultural condition.

According to Claro et al. (2003), the characteristics of the environment in which the agri-food chain operates affect its competitive and relational characteristics and identify relationships that the firm establishes with external actors in the accomplishment of its economic activities. In this respect, particular attention is to be paid to technological conditions, the role of associations, and the terms of access to credit (Hobbs and Young 2000).

In light of these considerations, we formulated the following first general research hypothesis:

H1 environmental variables affect both the type of relational governance and the economic performance of firms.

Given the peculiar traits of the supply chain targeted in this study, the first general research hypothesis is broken down into the set of specific research hypotheses described in Table 1.
Table 1

Research hypotheses investigating the effects of environmental conditions on relational governance and firm economic performance

HP code

Exogenous

variable

 

Endogenous

variable

HP description

H1.1

Governance

(+)

Milk price

More formal types of governance are associated with better economic performance of farms, i.e., higher milk prices

H1.2.1

Firm size

(+)

Governance

Larger firms are more likely to adopt more formal types of governance in their relation with processors

H1.2.2

Firm size

(+)

Milk price

Larger farms are more likely to obtain higher prices for their milk

H1.3.1

Technology

(+)

Governance

Breeders using modern technologies are more likely to adopt more formal types of governance in their relation with processors

H1.3.2

Technology

(+)

Milk price

Breeders using modern technologies are more likely to obtain higher prices

H1.3.3

Technology

(+)

Firm size

Breeders using modern technologies are more likely to run larger farms

H1.4.1

Association

(+)

Governance

Breeders appreciating the support of trade association are more likely to adopt less formal types of governance in their relation with processors

H1.4.2

Association

(+)

Milk price

Breeders appreciating the support of trade association are more likely to obtain higher prices

H1.4.3

Association

(+)

Firm size

Breeders appreciating the support of trade association are more likely to run larger farms

H1.5.1

Credit

(+)

Governance

Breeders appreciating the financial support of banks are more likely to adopt more formal types of governance in their relation with processors

H1.5.2

Credit

(+)

Milk price

Breeders appreciating the financial support of banks are more likely to obtain higher prices

H1.5.3

Credit

(+)

Firm size

Breeders appreciating the financial support of banks are more likely to run larger farms

A second important dimension affecting both the type of governance of supply chain dyadic relationships and the economic performance of firms according to various contributions in the economic literature relates to product characteristics.

Hobbs and Young (2000) and Hunt et al. (2005) evaluated the complex system of interdependence and inter-organizational relationships within agri-food networks, where product characteristics (quality, safety, differentiation, etc.) are introduced as a main factor affecting vertical coordination and supply-chain performance. A further interesting contribution in this direction was provided by Han et al. (2011), who investigated the relationship between quality management practices and the degree of vertical integration.

Fisher (1997) provides a detailed analysis of the impact that the characteristics of products have on the choice of strategies to be adopted in the supply chain. More precisely, the author suggests that the most appropriate supply chain strategy for functional products (with predictable demand) would be the pursue of physically efficient processes, whereas in case of innovative products (with unpredictable demand), supply chain should pursue market-responsiveness.

In fact, lifestyle changes in the recent decades and the new technologies available have induced companies to adapt their supply in order to meet new consumer preferences (Molnár et al. 2011). This is particularly important in the agri-food industry, where features such as origin, quality, and safety play an important role and depend largely on the technologies used in the production process (Aramyan et al. 2007).

Based on these arguments, we formulate the following second general research hypothesis:

H2 the product features affect both the type of relational governance and the economic performance of firms

Given the peculiar traits of the supply chain analyzed, the second general research hypothesis is broken down into the set of specific hypotheses described in Table 2.
Table 2

Research hypotheses investigating the effects of product features on relational governance and firm economic performance

HP code

Exogenous variable

 

Endogenous variable

HP description

H2.1

Governance

(+)

Milk price

More formal types of governance are associated with better economic performance of farms, i.e., higher milk prices

H2.2.1

Firm size

(+)

Governance

Larger firms are more likely to adopt more formal types of governance in their relation with processors

H2.2.2

Firm size

(+)

Milk price

Larger farms are more likely to obtain higher prices for their milk

H2.3.1

Quality

(−)

Governance

Breeders producing milk of higher quality are more likely to adopt less formal types of governance in their relation with processors

H2.3.2

Quality

(+)

Milk price

Breeders producing milk of higher quality are more likely to obtain higher prices

H2.3.3

Quality

(−)

Firm size

Breeders producing milk of higher quality are more likely to run smaller farms

H2.4.1

Safety

(+)

Governance

More frequent milk safety tests are more likely to occur within more formal types of governance in breeder-processor relationships

H2.4.2

Safety

(+)

Milk price

More frequent milk safety tests are more likely to be associated with higher milk prices

H2.4.3

Safety

(+)

Firm size

More frequent milk safety tests are more likely to be performed for larger farms

H2.5.1

Local origin

(−)

Governance

Breeders oriented towards products and processes of local origin are more likely to adopt less formal types of governance in their relation with processors

H2.5.2

Local origin

(+)

Milk price

Breeders oriented towards products and processes of local origin are more likely to obtain higher prices

H2.5.3

Local origin

(−)

Firm size

Breeders oriented towards products and processes of local origin are more likely to run smaller farms

The third dimension considered as an antecedent for relational governance and economic performance of firm relates to producer-processor relationships.

Handfield and Bechtel (2002) made an important contribution on this issue by stressing that the management of relational forms of governance based on trust leads to substantial improvements in the responsiveness of the entire supply chain, thereby improving lead time and, consequently, the performance of all the parties involved.

Cai et al. (2009) demonstrated the beneficial effect of collaborative practices on the performance of agents involved in quasi-integrated forms of coordination.

Nyaga et al. (2010) showed that collaborative activities, such as information sharing, joint report of efforts, and dedicated investments, create trust and involvement that generate satisfaction and improve performance.

Furthermore, Narasimhan and Nair (2005) showed that geographical proximity is a key factor in the creation of forms of governance, such as strategic alliances, that have a positive impact on the financial performance of companies. According to Buvik and Reve (2002), relational links ensure the best performance for both partners in terms of sharing communication and maintaining long-term relationships with suppliers. The relevance of geographical proximity in influencing local production system competitive performances is at the core of a vast body of literature related to clusters or industrial districts (Marshall 1920); Becattini 1989; Porter 1998) and in particular the work of Farrell (Farrell 2005) where spatial proximity can generate efficient informal contractual relationships, mostly in Italian districts where the social cohesion is high and the legal system relatively inefficient.

Based on these arguments, we formulate the following third general research hypothesis:

H3 the characteristics of producer-processor relationships affect both the type of relational governance and the economic performance of firms

Given the peculiar traits of the supply chain targeted in this study, the second general research hypothesis is broken down into the set of research hypotheses described in Table 3.
Table 3

Research hypotheses investigating the effects of producer-processor relationship characteristics on relational governance and firm economic performance

HP code

Exogenous variable

 

Endogenous variable

HP description

H3.1

Governance

(+)

Milk price

More formal types of governance are associated with better economic performance of farms, i.e., higher milk prices

H3.2.1

Firm size

(+)

Governance

Larger farms are more likely to adopt more formal types of governance in their relation with processors

H3.2.2

Firm size

(+)

Milk price

Larger farms are more likely to obtain higher prices for their milk

H3.3.1

Trust

(−)

Governance

Breeders that trust their commercial partners are more likely to adopt less formal types of governance in their relation with processors

H3.3.2

Trust

(+)

Milk price

Breeders that trust their commercial partners are more likely to obtain higher prices for their milk

H3.3.3

Trust

(+)

Firm size

Breeders that trust their commercial partners are more likely to run larger farms

H2.4.1

Uncertainty

(+)

Governance

The higher the uncertainty perceived by farmers, the more they are likely to adopt more formal types of governance in their relationships with processors

H2.4.2

Uncertainty

(−)

Milk price

The higher the uncertainty perceived by farmers, the more they are likely to obtain lower milk prices

H2.4.3

Uncertainty

(−)

Firm size

The higher the uncertainty perceived by farmers, the more they are likely to run smaller farms

H2.5.1

Investment

(+)

Governance

Breeders who made investments to meet the needs of their commercial partners are more likely to adopt more formal types of governance in their relation with processors

H2.5.2

Investment

(+)

Milk price

Breeders who made investments to meet the needs of their commercial partners are more likely to obtain higher prices

H2.5.3

Investment

(+)

Firm size

Breeders who made investments to meet the needs of their commercial partners are more likely to run larger farms

Data and measures

The empirical evidence needed to assess the research hypotheses formulated was collected through a direct survey carried out by means of a questionnaire.

Given the regional scope of the analysis and the objectives of this study, we chose a judgment sampling approach. In order to select a sample of both farmers and processors actually related to each other in supply-chain transactions and given the importance of the PDO consortia described above, we chose to focus on the processing firms that are members of these consortia and on the farmers supplying them with milk. Drawing from the information provided by the PDO consortia, we invited all 66 processors within the regional PDO supply chain to participate in the survey. As for breeders, we chose to focus only on farms with at least 300 sheep. In fact, this threshold has been used in previous studies to designate small- or medium-sized enterprises that practice farming as their main agricultural activity (Idda et al. 2010). Applying this selection criterion to the breeders that supply milk to processors within the PDO consortia, we targeted a total of 497 farms. Finally, willingness to participate in the survey was verified by telephone, with a 15.3% positive response rate from farmers (corresponding to 76 units) and 30.3% for processors (20 units), resulting in an overall sample size of 96 statistical units (N = 96).

The questionnaire was designed based on a literature review, prior case studies (Claro et al. 2003; Fischer et al. 2009), and also drawing from interviews with regional stakeholders (officers of the regional authority, representatives of PDO consortia, and members of trade associations).

The questionnaires were administered personally; this choice was due to the length and complexity of the structure of the questionnaire itself, which suggested the need to provide support for the interviewees. Prior to conducting the formal investigation, trial interviews were performed on 18 firms, and the final questionnaire was revised based on the results obtained.

The variables assessed and the measurement scales used in the questionnaire are displayed in Table 4.
Table 4

Items and scales used in the questionnaire

Variables

Items

Scale

Mean

St. Dev.

Governance

GOV

What type of relation do you have with your commercial partners?

“1 = verbal agreements,”

“2 = cooperative membership,”

“3 = formal contracts”

1.95

0.77

Milk price

MLP

What was the average price of milk in the last 2 years?

Quantitative/continuous

0.72

0.79

Firm size

SIZE

What is the size of your firm (number of sheep)?

“1 = small,”

“2 = medium,”

“3 = large”

2.01

0.72

Dimensions

 Environment

  Technology

TECH

(Multi item, α = 0.75)

   
 

TECH1

What type of technology do you use in breeding?

“1 = manual”,

“2 = semi-automated”,

“3 = automated”

1.36

0.51

 

TECH2

What type of technology do you use in milk production?

“1 = manual”,

“2 = semi-automated”,

“3 = automated”

1.31

0.49

  Association

ASC

Trade associations provide an excellent support for firms

Five-point Likert:

from “1 = strongly disagree”

to “5 = strongly agree”

2.77

1.34

  Credit

CRE

Banks provide an excellent financial support

Five-point Likert:

from “1 = strongly disagree”

to “5 = strongly agree”

1.90

0.88

 Product

  Local origin

LOC

(Multi item, α = 0.76)

   
 

LOC1

I have a strong connection with Sardinian agricultural and sheep-rearing traditions

Five-point Likert:

from “1 = strongly disagree”

to “5 = strongly agree”

4.39

0.80

 

LOC2

I use production methods arising from the local cultural context

five-point Likert:

from “1 = strongly disagree”

to “5 = strongly agree”

4.36

0.85

  Safety

SAFE

My commercial partners perform regular milk quality tests

Five-point Likert:

from “1 = never”

to “5 = always”

4.66

0.61

  Quality

QUA

(Multi item, α = 0.95)

   
 

QUA1

Milk is produced with wild breeding

Five-point Likert:

from “1 = never”

to “5 = always”

4.35

1.11

 

QUA2

Milk produced with wild breeding is of superior quality

Five-point Likert:

from “1 = strongly disagree”

to “5 = strongly agree”

4.45

0.98

Relationship

 Trust

TRU

(Multi item, α = 0.94)

   
 

TRU1

My commercial partners provide correct information

Five-point Likert:

from “1 = strongly disagree”

to “5 = strongly agree”

3.63

1.17

 

TRU2

My commercial partners fulfill their promises

Five-point Likert:

from “1 = strongly disagree”

to “5 = strongly agree”

3.59

1.25

 Uncertainty

UNC

I know in advance the price of milk

Five-point Likert:

from “1 = strongly agree”

to “5 = strongly disagree”

2.23

1.45

 Investments

INV

I made investments to meet the needs of my commercial partners

Five-point Likert:

from “1 = strongly disagree”

to “5 = strongly agree”

2.05

1.56

The types of relational governance observed and included in the analysis range from spot market transactions to closer vertical integration. Despite being a latent variable, the governance construct coincides with the observable ways in which businesses establish their relationships with their partners in the supply chain, namely “verbal agreements,” “cooperative membership,” and “formal contracts”.

In order to measure the firm’s economic performance using a variable that the actors themselves consider correct and reliable, we refer to the milk price as a synthetic indicator.

The performance construct was measured by the average price of milk reported over the previous 2 years.

Following Claro et al. (2003), firm size was chosen as the control variable in order to assess its mediating role between governance and firm performance. More precisely, farm size was measured by the number of sheep, processor size was measured by the number of employees, and each was divided into three categories of small, medium, and large firms.

As far as the exogenous variables are concerned, the features of the environment identified as influential for the supply chain include technology, credit access, and membership in trade associations.

The items identified as influential within the product characteristics dimension are related to product local origin, safety, and quality.

Finally, the characteristics of farmer-processor relationships considered are trust in commercial partners, uncertainty, and relationship-specific investments.

Data collected have been processed with IBM SPSS 22.0 and AMOS 22.0 software.

First of all, EFA was conducted to determine what items should be included in the models and what items to discard when they did not load on the investigated dimension. The EFA was also performed to assess the number and the validity of the underlying multidimensional constructs. Finally, the Cronbach’s alpha index was used to assess the reliability of the emerging measurement scale. As a second step of the analysis, we framed and estimated a SEM per each set of hypotheses formulated. Specifying the model followed an iterative process based on theoretical and empirical analyses until the structural model fit was positively tested.

This way of handling the models aims to reduce the distinction between a confirmatory approach (only one model tested) and exploratory approach. Comparing several models and/or the existence of equivalent models improves the fit of the structural model to empirical data or to the theory that underlies it. Indexes exist to identify variables that are worth the effort of re-specification. Indeed, it is possible to add or withdraw paths based on empirical criteria (de Marco et al. 2009).

Many absolute and incremental fit indices exist, and to date, a consensus has not been reached regarding which should be reported or what normative threshold standards should be considered (Chin et al. 2008; Hooper et al. 2008).

The former group of indicators includes the following statistics: the chi-square fit test index (CMIN/DF), the normed fit index (NFI), the comparative fit index (CFI), and the root mean square error of approximation (RMSEA).

The chi-square index tests whether an unconstrained specified model fits the covariance/correlation matrix as well as the empirical data. A problem with this test is that the larger the sample size, the more likely it is for the model to be rejected. For these reasons, the chi-square fit test (CMIN/DF) adjusts the chi-square index for the degrees of freedom. Values as large as five are accepted as an adequate fit, but more conservative thresholds are two or three (Arbuckle 2009). The NFI and CFI vary from 0 to 1 and are derived from a comparison of the hypothesized model with the independent model; however, a major drawback to this index is that it is sensitive to sample size, as it underestimates fit for samples of less than 200 units (as in this case). Hence, it is important to calculate the CFI, a revised form of the NFI, which takes into account sample size and is considered to perform well even when the sample size is small (Hooper et al. 2008).

The RMSEA incorporates a discrepancy function criterion (comparing observed and predicted covariance matrices) and a parsimony criterion; it should be less than or equal to 0.05 (0.08) for a good (adequate) model fit (Hu and Bentler 1999).

Results and discussion

The performed EFA suggests that the majority of items load on the appropriate dimensions under investigations, supporting the specification of the three different models (Table 5).
Table 5

Results of the exploratory factor analysis (EFA)

 

Product

Relationship

Environment

QUA1

0.876

0.133

− 0.027

− 0.235

QUA2

0.853

0.203

− 0.025

− 0.272

LOC1

0.826

0.196

0.008

0.109

LOC2

0.783

0.059

− 0.153

0.033

TRU1

0.205

0.877

0.088

0.144

TRU2

0.029

0.868

0.001

0.249

UNC

− 0.051

− 0.785

0.230

0.414

INV

0.077

0.227

0.782

0.087

TECH1

− 0.373

− 0.041

0.655

0.284

TECH2

− 0.577

0.004

0.622

0.258

SAFE

− 0.010

0.183

− 0.575

0.145

CRE

− 0.132

− 0.035

0.238

0.852

ASC

− 0.089

0.268

− 0.108

0.765

Extraction method: principal component analysis

Rotation method: Varimax with Kaiser Normalization

Rotation converged in 6 iterations

Moreover, in order to improve the fit of the three structural models to empirical data, some variables (and the related paths and parameters) that were worth the effort of re-specification were withdrawn according to indexes of modification (e.g., standardized residues of the covariance matrix).

The results of the estimation of the three SEMs are presented in the following figure and tables. The first three hypotheses considered, concerning the relations between the type of relational governance and milk price, as well as the mediating effect of firm size, show a quite consistent pattern in each model.

Quite interestingly, contrary to our expectations (H1.1, H2.1, H3.1), we observe a negative effect of governance on milk price. However, given the specific characteristics of the context analyzed, this result can be easily explained as it shows how less formal contractual arrangements can positively influence the contractual relationship in favor of milk producers.

The firm size positive relation with governance supports the assumption formulated (H1.2.1, H2.2.1, H3.2.1) suggesting a more widespread adoption of formal contracts between large farms and traders operating outside the local market boundaries, where the spatial proximity influence on the efficiency of informal contractual arrangements does not apply.

The expected positive relation between firm size and milk price is confirmed only when considering the influence of the product dimension (H2.2.2).

The results of the first model assessing the effect of the dimension “environment” are displayed in Fig. 1 and Table 6.
Fig. 1
Fig. 1

H1 “environment” SEM (a) initial specification (b) re-specification

Table 6

H1 SEM model fit indicators

Indicator

Cut-off value

Calculated value

(a) Initial specification

 CMIN/DF

≤ 2.00 (Bagozzi and Yi 1988)

9.025

 NFI

≥ 0.90 (Byrne 1994)

0.692

 CFI

≥ 0.90 (Byrne 1994)

0.695

 RMSEA

≤ 0.08 (Hu and Bentler 1999)

0.291

(b) Re-specification

 CMIN/DF

≤ 2.00 (Bagozzi and Yi 1988)

1.506

 NFI

≥ 0.90 (Byrne 1994)

0.949

 CFI

≥ 0.90 (Byrne 1994)

0.981

 RMSEA

≤ 0.08 (Hu and Bentler 1999)

0.073

The positive relation between technology and firm size confirms the capacity of larger firms to adopt more sophisticated and expensive technologies and more easily relate to the financial system (H1.3.3).

The positive relation between association and milk price (H1.4.2) on the other hand shows an expected positive influence of external economies of scale (joint access to market) on the firms’ bargaining power.

The positive relation between association and firm size supports the hypothesis formulated (H1.4.3), and it can be explained by the relatively higher managerial skill and less conservative/individualistic attitude of larger firm owners.

It remains to be understood whether the large-sized companies, more frequently members of associations, are also characterized by a prevalence of informal contractual arrangements.

The results of the second model assessing the effect of the dimension “product” are displayed in Fig. 2 and in Table 7.
Fig. 2
Fig. 2

H2 “product” SEM (a) initial specification (b) re-specification

Table 7

H2 SEM model fit indicators

Indicator

Cut-off value

Calculated value

(a) Initial specification

 CMIN/DF

≤ 2.00 (Bagozzi and Yi 1988)

6.220

 NFI

≥ 0.90 (Byrne 1994)

0.804

 CFI

≥ 0.90 (Byrne 1994)

0.822

 RMSEA

≤ 0.08 (Hu and Bentler 1999)

0.234

(b) Re-specification

 CMIN/DF

≤ 2.00 (Bagozzi and Yi 1988)

1.594

 NFI

≥ 0.90 (Byrne 1994)

0.963

 CFI

≥ 0.90 (Byrne 1994)

0.985

 RMSEA

≤ 0.08 (Hu and Bentler 1999)

0.079

The negative relation between quality and firm size (H2.3.3) could be explained following the findings of Aramyan (Aramyan 2007) where the smaller firms’ capacity to provide a more efficient input use (technical efficiency) could also translate in a better product’s quality.

The negative relation between safety and governance contradicts our original assumption (H2.4.1). However, it is consistent with the idea that more informal contractual arrangements, related to local markets characterized by higher social cohesion and control, can positively influence the products’ safety.

Finally, the results of the second model assessing the effect of the dimension “product” are displayed in Fig. 3 and in Table 8.
Fig. 3
Fig. 3

H3 “relationship” SEM (a) initial specification (b) re-specification

Table 8

H3 SEM model fit indicators

Indicator

Cut-off value

Calculated value

(a) Initial specification

 CMIN/DF

≤ 2.00 (Bagozzi and Yi 1988)

5.296

 NFI

≥ 0.90 (Byrne 1994)

0.889

 CFI

≥ 0.90 (Byrne 1994)

0.905

 RMSEA

≤ 0.08 (Hu and Bentler 1999)

0.213

(b) Re-specification

 CMIN/DF

≤ 2.00 (Bagozzi and Yi 1988)

0.972

 NFI

≥ 0.90 (Byrne 1994)

0.990

 CFI

≥ 0.90 (Byrne 1994)

1.000

 RMSEA

≤ 0.08 (Hu and Bentler 1999)

0.000

Trust is negatively related to the level of formalization of the contractual relationship, thus confirming our original assumption (H3.3.1) and the findings of Farrell (Farrell 2005) stating that in local production (and consumption) systems characterized by social cohesion and a relatively inefficient legal systems, informal contractual arrangements are more efficient than formal contracts.

The positive relation between trust and firm size (H3.3.3) can be explained by the possibly more efficient management of both technical and administrative relations between farmers and processors/traders and larger farmers.

The same positive influence emerged when considering trust and milk price, as we expected (H3.3.2).

Conclusions

This study assessed the causal relationships between the type of relational governance adopted by farms and their economic performance, considering the influence of the three exogenous dimensions: environment, product characteristics, and producer-processor relationships in agri-food supply chains.

The estimations obtained for the traditional dairy sheep supply chain in Sardinia confirm the relevance of most of the relationship assessed under each of the three exogenous dimensions.

The results concerning the relations between the type of relational governance and milk price, as well as the mediating effect of firm size, show a quite consistent pattern in each model.

Quite interestingly, contrary to our expectations, we observe a negative effect of governance on milk price. However, given the specific characteristics of the context analyzed, this result can be easily explained as it shows how less formal contractual arrangements can positively influence the contractual relationship in favor of milk producers. This result is in line with the findings of Farrell (Farrell 2005) who pointed out that in local production (and consumption) systems characterized by social cohesion, informal contractual arrangements are more efficient than formal contracts.

Another interesting result concerns the expected positive relation between firm size and milk price that is confirmed only when considering the influence of the product dimension.

Overall, the most important environmental factors are technological endowments and association; on the other hand, product features are determined mostly by quality and safety, whereas the relationships are affected mostly by trust.

Relational models in economic theory highlight the importance of trust as a key variable for attaining collaborative relationships and mutual benefits for the parties involved. The study conducted provides further evidence in support of this assumption and suggests that the development of successful cooperation in the Pecorino Romano DOP supply chain in Sardinia is hindered by a lack of trust in farmer-processor relationships.

This calls for a total rethinking of the relationships within the sector, particularly between farmers and processors, who should abandon any conflicts and pursue collaborative efforts. From this perspective, appropriate measures to improve the economic performance of the sector would entail a redefinition of product quality and marketing strategies. The Consortium of Pecorino Romano has already proposed interesting guidelines for repositioning the product on the market by means of quality incentives, organic production, and product differentiation according to aging. Such a repositioning process would also be aided by the implementation of stronger vertical coordination along the supply chain aimed at distributing appropriate returns for both farmers and processors. The method of determining the price should be revised accordingly, for example, with the development of an inter-professional agreement.

Limitations of the study and prospects for future research

Even though the models adopted and the estimations performed provided satisfactory results, the overall interpretative capacity of this study is hindered by the empirical evidence available. More precisely, we acknowledge that the sample surveyed has two main limitations.

First, it cannot be considered fully representative of the sheep breeding activity in Sardinia. This is not so much due to the overall number of observations gathered, that is comparable to that of previous studies (Idda et al. 2010; Mantecón et al. 2009; Milán et al. 2011; Riveiro et al. 2013). Rather, the limitation is due to the choice of cutting farms with less than 300 sheep off the sample. This choice was made to focus on enterprises practicing sheep breeding as their main agricultural activity and having structured relations with cheese manufacturers. However, it probably entails a partial representation of the sector, that is actually composed of many small family farms.

A second limitation concerns the need to reduce the number of variables in the models to meet statistical requirements, creating a risk of over-simplifying the phenomena investigated.

This is why future research should be conducted on larger and more representative samples, to provide more in-depth insights on further interesting factors, such as investments, uncertainty, and performance.

As far as investments are concerned, the economics of transaction costs and the supply chain management approach regard the specificity of assets as one of the most important variables in the analysis of strategic transactions and modes of governance. However, the results of the SEM estimation suggested that relationships among firms in the supply chain were not influenced significantly by specific investments, i.e., investments made to meet the requirements of the commercial partners. Hence, it would be interesting to understand the reasons underlying the lack of specific investments in this sector by deepening the theoretical analysis of the relative inter-organizational relationships and empirically assessing the relapses on the sector. In this regard, the resource-based view provides interesting elements that can be included in the conceptual framework (Wernerfelt 1995), whereas an empirical comparison of various contexts with similar competitive conditions could be useful for evaluating the role of specific investments in inter-organizational relationships.

Other issues that could be further developed in future research relate to uncertainty. In principle, uncertainty may refer to both the firms’ behavior and the environmental context in which they operate. In the former case, uncertainty involves the firms’ limited knowledge of the behavior of their competitors and partners, which raises the well-known risks of moral hazard. In the latter case, uncertainty implies the firms’ limited knowledge of external changes, such as those in regulations and the market (e.g., consumption and competition). The decision to include only behavioral uncertainty in this study stems from the information gathered on the sector of interest, which indicates the existence of information asymmetries as well as considerably tense relationships between farmers and processors, despite relatively stable overall external conditions in terms of consumer preferences, trade agreements, and sales trends. Hence, given the strength of the Pecorino Romano PDO brand, we chose to disregard environmental uncertainty and focus on relational uncertainty determinants and implications. However, we must acknowledge that the results obtained did not suggest a significant role for environmental uncertainty in the sector, probably due to new consumption patterns and the increasing market demand for quality. Thus, the inclusion of environmental uncertainty issues in future research could provide a new interpretive key for creating collaborative relationships in the dairy sheep supply chain in Sardinia as well.

A further consideration is that milk prices do not seem to be a variable that can convey the complexity of this sector. Hence, other factors should be considered to assess the economic performance of firms or, better, their relational performance. Furthermore, we could argue that performance measures should be carefully selected according to the specific supply chain and the life cycle stage of the product. As suggested by Aramyan et al. (Aramyan et al. 2007), these factors include not only efficiency and product quality but also responsiveness and flexibility. More precisely, in the case of agri-food products that have already reached their maturity stage (or initial decline), the performance of inter-organizational relationships is significantly determined by the availability and management of both tangible and intangible resources (e.g., knowledge, skills, and know-how) that can support the development of collaborative relationships and forms of governance with greater flexibility.

Declarations

Authors’ contributions

All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
Department of Agricultural and Food Sciences, University of Bologna, viale G. Fanin, 50, 40127 Bologna, Italy

References

  1. Albisu LM, Frohberg K, Hartmann M (2010) Building sustainable relationships in agri-food chains: challenges from farm to retail. In: Fisher C, Hartmann M (eds) Agri-food chain relationships. CAB International, Wallingford, UK, p 25Google Scholar
  2. Aramyan LH (2007) Measuring supply chain performance in the agri-food sector. PhD thesis Wageningen University, WageningenGoogle Scholar
  3. Aramyan LH, Kuiper M (2009) Analyzing price transmission in agri-food supply chains: an overview. Meas Bus Excell 13(3):3–12View ArticleGoogle Scholar
  4. Aramyan LH, Lansink A, van der Vorst J, van Kooten O (2007) Performance measurement in agri-food supply chains: a case study. Supply Chain Manage Int J 12(4):304–315View ArticleGoogle Scholar
  5. Arbuckle JL (2009) AMOS 18 user’s guide. Amos Development Corporation, CrawfordvilleGoogle Scholar
  6. Bagozzi RP, Yi Y (1988) On the evaluation of structural equation models. J Acad Mark Sci 16(1):74–94View ArticleGoogle Scholar
  7. Becattini G (1989) Modelli locali di sviluppo. Il Mulino, BolognaGoogle Scholar
  8. Bernués A, Boutonnet JP, Casasús I, Chentouf M, Gabiña D, Joy M, López-Francos A, Morand-Fehr P, Pacheco F (2011) Economic, social and environmental sustainability in sheep and goat production systems. Zaragoza: CIHEAM / FAO / CITA-DGA, 2011. 379 p. (Options Méditerranéennes : Série A. Séminaires Méditerranéens; n. 100). 7. Proceedings of the International Seminar of the Sub-Network on Production Systems of the FAO-CIHEAM Inter-Regional Cooperative Research and Development Network on Sheep and Goats, 2010/11/10-12 Zaragoza (Spain)Google Scholar
  9. Buvik A, Reve T (2002) Inter-firm governance and structural power in industrial relationships: the moderating effect of bargaining power on the contractual safeguarding of specific assets. Scand J Manag 18:261–284View ArticleGoogle Scholar
  10. Byrne BM (1994) Structural equation modeling with EQS and EQS/Windows. Sage Publications, Thousand Oaks, CAGoogle Scholar
  11. Cai S, Yang Z, Hu Z (2009) Exploring the governance mechanisms of quasi-integration in buyer-supplier relationships. J Bus Res 62:660–666View ArticleGoogle Scholar
  12. Carbone A (2017) Food supply chains: coordination governance and other shaping forces. Agric Food Econ 5:3. https://doi.org/10.1186/s40100-017-0071-3 View ArticleGoogle Scholar
  13. Carr AS, Pearson JN (1999) Strategically managed buyer–supplier relationships and performance outcomes. J Oper Manag 17(5):497–520View ArticleGoogle Scholar
  14. Chaddad F, Rodriquez-Alcala ME (2010) Inter-organizational relationships in agri-food systems: a transaction cost economics approach. In: Fischer C, Hartmann M (eds) Agri-food chain relationships, pp 45–60View ArticleGoogle Scholar
  15. Chin WW, Peterson RA, Brown SP (2008) Structural equation modeling in marketing: some practical reminders. J Mark Theory Pract 16(4):287–298View ArticleGoogle Scholar
  16. Claro DP, Hagelaar G, Omta O (2003) The determinants of relational governance and performance: how to manage business relationships? Ind Mark Manag 32:703–716.View ArticleGoogle Scholar
  17. de Marco G, Vrignaud P, Destrieux C, de Marco D, Testelin S, Devauchelle B, & Berquin P (2009). Principle of structural equation modeling for exploring functional interactivity within a putative network of interconnected brain areas. Magnetic Resonance Imaging, 27(1), 1–12. https://doi.org/10.1016/J.MRI.2008.05.003.View ArticleGoogle Scholar
  18. de Rancourt M, Carrère L (2011) Milk sheep production systems in Europe: diversity and main trends. In: Bernués A, Boutonnet JP, Casasús I, Chentouf M, Gabiña D, Joy M, López-Francos A, Morand-Fehr P, Pacheco F (eds), Economic, social and environmental sustainability in sheep and goat production systems. Zaragoza : CIHEAM / FAO / CITA-DGA, 2011. p. 107–111 (Options Méditerranéennes: Série A. Séminaires Méditerranéens; n. 100). Available at: http://om.ciheam.org/om/pdf/a100/00801490.pdf
  19. Farrell H (2005) Trust and political economy, institutions and the sources of interfirm cooperation. Comp Pol Stud 38(5):459–483View ArticleGoogle Scholar
  20. Fischer C, Hartmann M, Reynolds N, Leat P, Roveredo-Giha C, Henchion M, Albisu LM, Gracia A (2009) Factors influencing contractual choice and sustainable relationships in European agri-food supply chains. Eur Rev Agric Econ 36(4):541–569View ArticleGoogle Scholar
  21. Fisher ML (1997) What is the right supply chain for your product? Harv Bus Rev 75(2):105–116Google Scholar
  22. Fontana F, Caroli M (2003) Economia e Gestione delle imprese. McGraw-Hill, MilanoGoogle Scholar
  23. Frendi F, Milán MJ, Caja G, González R (2011) Performances économiques des exploitations d’ovins laitiers de races Assaf et Awassi dans Castille et Léon, Espagne. Options Méditerranéennes: Série A. Séminaires Méditerranéens, 100, pp. 191–198Google Scholar
  24. Furesi R, Madau F, Pulina P (2013) Technical efficiency in the sheep dairy industry: an application on the Sardinian (Italy) sector. Agric Food Econ 1:4 http://www.agrifoodecon.com/content/1/1/4 Google Scholar
  25. Gereffi, G, Lee, J, Christian, M (2008) The governance structures of U.S.-based food and agriculture value chains and their relevance to healthy diets. Paper prepared for the Healthy Eating Research Program, Robert Wood Johnson FoundationGoogle Scholar
  26. Granovetter MS (1992) Problems of explanation in economic sociology. Networks and Organizations: structure, form and action. Harvard Business School Press: Boston, 25–56Google Scholar
  27. Han J, Trienekens JH, Omta O (2011) Relationship and quality management in the Chinese pork supply chain. Int J Prod Econ 134:312–321View ArticleGoogle Scholar
  28. Handfield RB, Bechtel C (2002) The role of trust and relationship in improving supply chain responsiveness. Ind Mark Manag 31:367–382View ArticleGoogle Scholar
  29. Hobbs JE, Young LM (2000) Closer vertical co-ordination in agri-food supply chains: a conceptual framework and some preliminary evidence. Supply Chain Manage Int J 5(3):131–142View ArticleGoogle Scholar
  30. Hooper D, Coughlan J, Mullen MR (2008) Structural equation modelling: guidelines for determining model fit. Electron J Bus Res Methods 6(1):53–60Google Scholar
  31. Hu L, Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Model Multidiscip J 6(1):1–55View ArticleGoogle Scholar
  32. Hunt I, Wall B, Jadgev H (2005) Applying the concepts of extended products and extended enterprises to support the activities of dynamic supply networks in the agri-food industry. J Food Eng 70(3):393–402View ArticleGoogle Scholar
  33. Idda L, Furesi R, Pulina P (2010) Economia dell’allevamento ovino da latte. Produzione, trasformazione, mercato. FrancoAngeli Editore, MilanoGoogle Scholar
  34. ISTAT (2012) Sesto Censimento Generale dell’Agricoltura. Available at: http://censimentoagricoltura.istat.it. Accessed 14 Apr 2017
  35. Jöreskog K G, van Thillo M (1972) LISREL: a general computer program for estimating a linear structural equation system involving multiple indicators of unmeasured variables. Research Bulletin No. 72–56, Princeton, NJ. Service, Educational TestingGoogle Scholar
  36. Kannan VR, Tan KC (2005) Just in time, total quality management, and supply chain management: understanding their linkages and impact on business performance. Omega 33(2):153–162View ArticleGoogle Scholar
  37. Liu Y, Luo Y, Liu T (2009) Governing buyer-supplier relationships through transactional and relational mechanism: evidence from China. J Oper Manag 27:294–309View ArticleGoogle Scholar
  38. Mantecón AR, Díez P, Villadangos B, Martínez Y, Lavín, P. (2009) Dairy sheep production systems in central-north Spain: effect of flock size, Changes in sheep and goat farming systems at the beginning of the 21st century. Options Méditerranéennes, Série A. Séminaires Méditerranéens, 91, pp. 75–77Google Scholar
  39. Marshall A (1920) Principles of economics, 8th edn. Macmillan, LondonGoogle Scholar
  40. Ménard C (2004) The economics of hybrid organizations. J Inst Theor Econ 160(3):345–376View ArticleGoogle Scholar
  41. Milán MJ, Caja G, González-González R, Fernández-Pérez AM, Such X (2011) Structure and performance of Awassi and Assaf dairy sheep farms in northwestern Spain. J Dairy Sci 94(2):771–784View ArticleGoogle Scholar
  42. Molnár A, Gellynck X, Vanhonacker F, Gagalyuk T, Verbeke W (2011) Do chain goals match consumer perceptions? The case of the traditional food sector in selected European Union countries. Agribusiness 27(2):221–243View ArticleGoogle Scholar
  43. Narasimhan R, Nair A (2005) The antecedent role of quality, information sharing and supply chain proximity on strategic alliance formation and performance. Int J Prod Econ 96:301–313View ArticleGoogle Scholar
  44. Nyaga GN, Whipple JM, Lynch D (2010) Examining supply chain relationships: do buyer and supplier perspectives on collaborative relationships differ? J Oper Manag 28:101–114View ArticleGoogle Scholar
  45. Porter ME (1998) The competitive advantage of nations (with a new foreword). The Free Press, New YorkView ArticleGoogle Scholar
  46. Pulina G, Rassu SPG, Rossi G, Brandano P (2011) La pastorizia sarda dell’ultimo secolo. In: Mattone A, Simbula PF (eds) La pastorizia mediterranea. Carocci, RomaGoogle Scholar
  47. Ripoll-Bosch R et al (2012) An integrated sustainability assessment of Mediterranean sheep farms with different degrees of intensification. Agric Syst 105(1):46–56View ArticleGoogle Scholar
  48. Riveiro JA, Mantecon AR, Alvarez CJ, Lavin P (2013) A typological characterization of dairy Assaf breed sheep farms at NW of Spain based on structural factor. Agric Syst 120:27–37View ArticleGoogle Scholar
  49. Toussaint G, Morand-Fehr P, Castel G, Choisis J P, Chentouf M, Mena Y, Pacheco F, Ruiz A (2009). Technical and economic analysis and assessment methodology for sheep and goat production systems. Options Méditerranéennes. Série A, Séminaires Méditerranéens, (91), 327–374Google Scholar
  50. Wernerfelt B (1995) The resource-based view of the firm: ten years after. Strateg Manag J 16(3):171–174View ArticleGoogle Scholar
  51. Zaheer A, McEvily B, Perrone V (1998) Does trust matter? Exploring the effects of interorganizational and interpersonal trust on performance. Organ Sci 9:141–159View ArticleGoogle Scholar

Copyright

Advertisement